Other packages > Find by keyword >

fastTS  

Fast Time Series Modeling for Seasonal Series with Exogenous Variables
View on CRAN: Click here


Download and install fastTS package within the R console
Install from CRAN:
install.packages("fastTS")

Install from Github:
library("remotes")
install_github("cran/fastTS")

Install by package version:
library("remotes")
install_version("fastTS", "1.0.3")



Attach the package and use:
library("fastTS")
Maintained by
Ryan Andrew Peterson
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2024-02-07
Latest Update: 2024-12-01
Description:
An implementation of sparsity-ranked lasso and related methods for time series data. This methodology is especially useful for large time series with exogenous features and/or complex seasonality. Originally described in Peterson and Cavanaugh (2022) in the context of variable selection with interactions and/or polynomials, ranked sparsity is a philosophy with methods useful for variable selection in the presence of prior informational asymmetry. This situation exists for time series data with complex seasonality, as shown in Peterson and Cavanaugh (2024) , which also describes this package in greater detail. The sparsity-ranked penalization methods for time series implemented in 'fastTS' can fit large/complex/high-frequency time series quickly, even with a high-dimensional exogenous feature set. The method is considerably faster than its competitors, while often producing more accurate predictions. Also included is a long hourly series of arrivals into the University of Iowa Emergency Department with concurrent local temperature.
How to cite:
Ryan Andrew Peterson (2024). fastTS: Fast Time Series Modeling for Seasonal Series with Exogenous Variables. R package version 1.0.3, https://cran.r-project.org/web/packages/fastTS. Accessed 22 Sep. 2026.
Previous versions and publish date:
(2026-07-09 07:37), 0.1.2 (2024-02-07 19:20), 1.0.0 (2024-03-07 19:00), 1.0.1 (2024-03-28 22:40), 1.0.2 (2024-12-02 00:10)
Other packages that cited fastTS R package
View fastTS citation profile
Other R packages that fastTS depends, imports, suggests or enhances
Complete documentation for fastTS
Functions, R codes and Examples using the fastTS R package
Some associated functions: fastTS . internal . predict.fastTS . uihc_ed_arrivals . 
Some associated R codes: data.R . fastTS.R . helpers.R . prediction.R .  Full fastTS package functions and examples
Downloads during the last 30 days

Today's Hot Picks in Authors and Packages

spider  
Species Identity and Evolution in R
Analysis of species limits and DNA barcoding data. Included are functions for generating important s ...
Download / Learn more Package Citations See dependency  
odns  
Access Scottish Health and Social Care Open Data
Allows potential users of Scottish Health and Social Care Open Data ( ...
Download / Learn more Package Citations See dependency  
heatmaply  
Interactive Cluster Heat Maps Using 'plotly' and 'ggplot2'
Create interactive cluster 'heatmaps' that can be saved as a stand- alone HTML file, embedded in 'R ...
Download / Learn more Package Citations See dependency  
jointseg  
Joint Segmentation of Multivariate (Copy Number) Signals
Methods for fast segmentation of multivariate signals into piecewise constant profiles and for gene ...
Download / Learn more Package Citations See dependency  
r2resize  
In-Text Resize for Images, Tables and Fancy Resize Containers in 'shiny', 'rmarkdown' and 'quarto' Documents
Automatic resizing toolbar for containers, images and tables. Various resizable or expandable contai ...
Download / Learn more Package Citations See dependency  
Countr  
Flexible Univariate Count Models Based on Renewal Processes
Flexible univariate count models based on renewal processes. The models may include covariates and ...
Download / Learn more Package Citations See dependency  

28,720

R Packages

247,686

Dependencies

75,677

Author Associations

28,721

Publication Badges

© Copyright since 2022. All right reserved, rpkg.net.  Based in Cambridge, Massachusetts, USA